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Update Keras categorical input design, mark it as complete. #267
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More importantly, add an official guide on how TF1 Estimator users with feature columns can convert their code into TF2 Keras users with KPL.
tanzhenyu
requested review from
ematejska,
ewilderj,
martinwicke and
theadactyl
as code owners
goldiegadde
reviewed
Jul 15, 2020
| * Users with large dimension categorical inputs will incur large memory footprint and computation cost, if wrapped with indicator column through `tf.keras.layers.DenseFeatures`. | ||
| * Currently there is no way to correctly feed Keras linear model or dense layer with multivalent categorical inputs or weighted categorical inputs. | ||
| * Currently there is no way to correctly feed Keras linear model or dense layer with multivalent categorical inputs or weighted categorical inputs, or shared embedding inputs. | ||
| * feature columns offer black-box implementations, mix feature engineering with trainable objects, and lead to |
goldiegadde
reviewed
Jul 15, 2020
ematejska
suggested changes
Jul 15, 2020
| # Keras categorical inputs | ||
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| | Status | Accepted | | ||
| | Status | Completed | |
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Do you mean it's implemented? I would prefer the terminology Implemented instead of Completed. Could you also post the PR number where it was implemented? Something like:
Status: Implemented (Link to PR)
ematejska
approved these changes
Jul 15, 2020
fchollet
approved these changes
Jul 15, 2020
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Only one minor change to split "IndexLookup" to "StringLookup" and "IntegerLookup". The rest is trivial.
But more importantly, add an official guide on how TF1 Estimator users with feature columns can convert their code into TF2 Keras users with KPL.